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wop 
posted an update 1 day ago
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1682
🧪 SlopFinder is here!!

We're building a dataset to study what humans actually consider AI slop.

SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.

Every vote helps build the dataset. 🧩

How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.

This is an early MVP, so the dataset is small and the system is still evolving.

Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)

Dataset:
bench-labs/slop-classification

@benchlabs
  • 17 replies
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mayafree 
posted an update 1 day ago
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1513
🧬 Architecture lineage of Korea's sovereign-AI foundation models — checked with public data

In late July 2026, as Korea released self-developed foundation models competing with DeepSeek and Qwen (e.g. LG K-EXAONE 2.0, 750B), interest grew — including a Zhihu thread with 2.7M+ views (→ https://www.zhihu.com/question/2067512422555029717 ) — over whether these models are trained from scratch or built on foreign open-weights.

Sharing a tool that answers this with public data rather than opinion.

🔗 Model Genome Korea → mayafree/Model-Genome-Korea

It classifies the public models of 9 Korean organizations that released "self-developed, from-scratch foundation models" on HuggingFace — 3 large enterprises (LG, NAVER, Kakao), 2 telcos (SKT, KT), 2 mid-size firms (NCSOFT, Upstage), 2 startups (Motif, VIDRAFT) — on two axes measured from public config.json + model weights:
• Architecture fingerprint — does model_type + (hidden·intermediate·layers) match a foreign open-weight model
• Weight fingerprint — embedding similarity (from-scratch vs continued-pretraining)

Genotypes: 🟢 Native · 🔵 Adapted · 🟡 Mixed · 🔴 Ported

The results are not uniform. Some models match foreign architectures (Qwen, Llama, …) exactly; others use self-built architectures and weights with no foreign match. Which company/model falls where is shown per model in the Space, along with attention originality, license, and reproducible open-source status.

This is a neutral transparency tool, not an accusation — building foundation models on open-weight bases is a legitimate, industry-standard practice. The exact same yardstick is applied to every model, without exception.

Features a 3D lineage graph, search, EN / 中文 / 한국어, and dark mode. Corrections are welcome via the Community tab.

Articles: https://huggingface.co/blog/mayafree/model-dna

#KoreanAI #LLM #ModelLineage #OpenSource #SovereignAI
  • 1 reply
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appvoid 
posted an update 1 day ago
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1168
I don't know if it was us or one of you guys or maybe all of us at once but lately we have seen a finetuning/pretraining explosion of models below 200m params and we can't be more happy about it keep coming tinkerers all of this is possible because of you!
  • 6 replies
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Banaxi-Tech 
posted an update 1 day ago
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1376
We're exited to announce BananaMind OS, our OS specically for running BananaMind models!
Its able to run BananaMind 2 Nano at 4 bit on only 7-8MB of ram, the 2 bit on 6MB of ram and the 8 bit version on 14MB of RAM!
It runs on a 486 or newer!
Check out this video and image running BananaMind 2 Nano 4 Bit on 9
MB of RAM and a emulated 486 in QEMU at ~1TPS!
We asked it: "What is the first letter of the alphabet?"
The response is:
"The first letter of the alphabet is:
- A.
"
And if you're asking because of the video, yes I am a arch btw.
Comment and like this post for a GitHub link and comment for adding other models!
  • 15 replies
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KlondikeDev 
posted an update 1 day ago
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722
Boris-2 coming soon!

The Models:

Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th.
Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th.
Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.

Why does 125M get more tokens than 250M?

Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.

Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!

We hope to end up in the ballpark of AxiomicLabs/GPT-X2.5-135M or BananaMind/BananaMind-2-Pro-Preview

Following this, we will release the Pro, Instruct and Pro-Instruct variants. More info will be coming soon!
  • 4 replies
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appvoid 
posted an update 2 days ago
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2786
If you want small models to be great again you should give a follow to people like @Banaxi-Tech or @Datdanboi25

These guys are rocking it with small models lately.

(They are not paying me to say that)
  • 22 replies
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Banaxi-Tech 
posted an update 3 days ago
Banaxi-Tech 
posted an update about 14 hours ago
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516
Today.
  • 3 replies
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ProCreations 
posted an update 1 day ago
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new day, new models! Introducing the Auto series, a series of classifiers for determining if agentic tool calls are safe to run or not to prevent any harmful actions from happening. They have high performance compared to other LLMs commonly used for this task with incredible speed and small memory footprints.
ProCreations/auto-1b (recommended generally, much higher accuracy)
ProCreations/auto-0.4b (faster but worse)
Comes with datasets as well (open source ftw)!
A GitHub repo with pi extensions etc will come soon with this model.
  • 2 replies
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Enderchef 
posted an update 5 days ago
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3168
GPT-X2.5-135M is finally released! 🚀
The new flagship from Axiomic Labs takes 3rd on the open SLM leaderboard trailing only the SmolLMs, check it out and follow us:
AxiomicLabs/GPT-X2.5-135M
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